# Enhanced Software Tools for Detecting Anatomical Differences in Image Data Sets

> **NIH NIH R42** · KITWARE, INC. · 2020 · $456,359

## Abstract

Project Summary
Morphometric analysis is a primary algorithmic tool to discover disease and drug related effects on brain
anatomy. Neurological degeneration and disease manifest in subtle and varied changes in brain anatomy that
can be non-local in nature and affect amounts of white and gray matter as well as relative positioning and
shapes of local brain anatomy. State-of-the-art morphometry methods focus on local matter distribution or on
shape variations of apriori selected anatomies but have difficulty in detecting global or regional deterioration of
matter; an important effect in many neurodegenerative processes. The proposal team recently developed a
morphometric analysis based on unbalanced optimal transport, called UTM, that promises to be capable of
discovering local and global alteration of matter without the need to apriori select an anatomical region of
interest. The goal of this proposal is to develop the UTM technology into a software tool for automated
high-throughput screening of large neurological image data sets. A more sensitive automated
morphometric analysis tool will help researchers to discover neurological effects related to disease and lead to
more efficient screening for drug related effects.

## Key facts

- **NIH application ID:** 10139715
- **Project number:** 2R42MH118845-03
- **Recipient organization:** KITWARE, INC.
- **Principal Investigator:** Beau M Ances
- **Activity code:** R42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $456,359
- **Award type:** 2
- **Project period:** 2018-09-18 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10139715

## Citation

> US National Institutes of Health, RePORTER application 10139715, Enhanced Software Tools for Detecting Anatomical Differences in Image Data Sets (2R42MH118845-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10139715. Licensed CC0.

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